Ranking documents based on user behavior and/or feature data

ABSTRACT

A system generates a model based on feature data relating to different features of a link from a linking document to a linked document and user behavior data relating to navigational actions associated with the link. The system also assigns a rank to a document based on the model.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.10/869,057, filed Jun. 17, 2004, now U.S. Pat. No. 7,716,225 issued May11, 2010, the disclosure of which is incorporated herein by reference.

BACKGROUND

1. Field of the Invention

Systems and methods consistent with the principles of the inventionrelate generally to information retrieval and, more particularly, tocreating a ranking function based on user behavior and/or feature dataand using the ranking function to assign ranks to documents.

2. Description of Related Art

The World Wide Web (“web”) contains a vast amount of information.Locating a desired portion of the information, however, can bechallenging. This problem is compounded because the amount ofinformation on the web and the number of new users inexperienced at websearching are growing rapidly.

Search engines attempt to return hyperlinks to web documents in which auser is interested. The goal of the search engine is to provide links tohigh quality documents to the user. Identifying high quality documentscan be a tricky problem and is made more difficult by spammingtechniques.

SUMMARY

According to one aspect, a method may include generating a model basedon user behavior data associated with a group of documents. The methodmay also include assigning weights to links based on the model, wherethe links may include references from first documents to seconddocuments in a set of documents, and assigning ranks to the seconddocuments based on ranks of the first documents and the weights assignedto the links.

According to another aspect, a system may include means for generating amodel based on user behavior data relating to links in a group ofdocuments and feature data associated with the links. The system mayalso include means for assigning weights to references in a set ofdocuments based on the model and means for assigning ranks to documentsin the set of documents based on the weights assigned to the references.

According to yet another aspect, a system may include a memory and aprocessor. The memory may store user behavior data relating to a groupof documents and feature data associated with the documents. Theprocessor may generate a model based on the user behavior data and thefeature data, assign a weight to a link from a first document to asecond document in a set of documents based on the model, and assign arank to the second document based on a rank of the first document andthe weight assigned to the link.

According to a further aspect, a method may include generating a modelbased on different types of feature data associated with a group ofdocuments. The method may also include assigning a weight to a link froma first document in a set of documents to a second document in the setof documents based on the model and assigning a rank to the seconddocument based on a rank of the first document and the weight assignedto the link.

According to another aspect, a method may include generating a modelbased on feature data relating to features of a linking document and alink associated with the linking document, where the linking documentreferences a linked document via the link. The method may also includeassigning a rank to a document based on the model.

According to yet another aspect, a method may include generating a modelbased on feature data relating to different features of a link from alinking document to a linked document and user behavior data relating tonavigational actions associated with the link and assigning a rank to adocument based on the model.

According to a further aspect, a method may include generating a modelbased on user behavior data and document feature data and assigningranks to documents based on the model.

According to another aspect, a method may include determining a weightfor a link from a linking document to a linked document based on featuredata associated with at least one of the link, the linking document, orthe linked document, and assigning a rank to at least one of the linkingdocument or the linked document based on the determined weight for thelink.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate an embodiment of the inventionand, together with the description, explain the invention. In thedrawings,

FIG. 1 is an exemplary diagram of a simple linked database;

FIG. 2 is a diagram of an exemplary information retrieval network inwhich systems and methods consistent with the principles of theinvention may be implemented;

FIG. 3 is an exemplary diagram of a client or server according to animplementation consistent with the principles of the invention;

FIG. 4 is a functional block diagram of an exemplary modeling systemaccording to an implementation consistent with the principles of theinvention;

FIG. 5 is a flowchart of exemplary processing for determining documentranks according to an implementation consistent with the principles ofthe invention;

FIG. 6 is a flowchart of exemplary processing for presenting searchresults according to an implementation consistent with the principles ofthe invention; and

FIG. 7 is a diagram of an exemplary linked database.

DETAILED DESCRIPTION

The following detailed description of the invention refers to theaccompanying drawings. The same reference numbers in different drawingsmay identify the same or similar elements. Also, the following detaileddescription does not limit the invention.

Overview

A linked database may include documents with links among them. A“document,” as the term is used herein, is to be broadly interpreted toinclude any machine-readable and machine-storable work product. Adocument may include, for example, an e-mail, a file, a combination offiles, one or more files with embedded links to other files, a newsgroup posting, a blog, a web advertisement, etc. In the context of theInternet, a common document is a web page. Web pages often includetextual information and may include embedded information (such as metainformation, images, hyperlinks, etc.) and/or embedded instructions(such as Javascript, etc.).

A “link,” as the term is used herein, is to be broadly interpreted toinclude any reference to/from a document from/to another document oranother part of the same document. A “forward link” (sometimes referredto as an “outbound link”) is used to refer to a link from a sourcedocument to a target document from the standpoint of the sourcedocument. A “backward link” (sometimes referred to as an “inbound link”)is used to refer to a link from a target document to a source documentfrom the standpoint of the source document.

FIG. 1 is an exemplary diagram of a simple linked database that includesthree documents: document A, document B, and document C. As shown,document A includes a single forward link to document C and two backwardlinks from documents B and C. Document B includes a single forward linkto document A. Document C includes a single forward link to document Aand a single backward link from document A.

Systems and methods consistent with the principles of the invention mayprovide a reasonable surfer model that indicates that when a surferaccesses a document with a set of links, the surfer will follow some ofthe links with higher probability than others. This reasonable surfermodel reflects the fact that not all of the links associated with adocument are equally likely to be followed. Examples of unlikelyfollowed links may include “Terms of Service” links, banneradvertisements, and links unrelated to the document.

Exemplary Information Retrieval Network

FIG. 2 is an exemplary diagram of a network 200 in which systems andmethods consistent with the principles of the invention may beimplemented. Network 200 may include multiple clients 210 connected tomultiple servers 220-240 via a network 250. Network 250 may include alocal area network (LAN), a wide area network (WAN), a telephonenetwork, such as the Public Switched Telephone Network (PSTN), anintranet, the Internet, a memory device, or a combination of networks.Two clients 210 and three servers 220-240 have been illustrated asconnected to network 250 for simplicity. In practice, there may be moreor fewer clients and servers. Also, in some instances, a client mayperform the functions of a server and/or a server may perform thefunctions of a client.

Clients 210 may include client entities. An entity may be defined as adevice, such as a personal computer, a wireless telephone, a personaldigital assistant (PDA), a lap top, or another type of computation orcommunication device, a thread or process running on one of thesedevices, and/or an object executable by one of these devices. Servers220-240 may include server entities that gather, process, search, and/ormaintain documents in a manner consistent with the principles of theinvention. Clients 210 and servers 220-240 may connect to network 250via wired, wireless, and/or optical connections.

In an implementation consistent with the principles of the invention,server 220 may include a search engine 225 usable by clients 210. Server220 may crawl a corpus of documents (e.g., web pages), index thedocuments, and store information associated with the documents in arepository of crawled documents. Servers 230 and 240 may store ormaintain documents that may be crawled by server 220. While servers220-240 are shown as separate entities, it may be possible for one ormore of servers 220-240 to perform one or more of the functions ofanother one or more of servers 220-240. For example, it may be possiblethat two or more of servers 220-240 are implemented as a single server.It may also be possible for a single one of servers 220-240 to beimplemented as two or more separate (and possibly distributed) devices.

Exemplary Client/Server Architecture

FIG. 3 is an exemplary diagram of a client or server entity (hereinaftercalled “client/server entity”), which may correspond to one or more ofclients 210 and servers 220-240, according to an implementationconsistent with the principles of the invention. The client/serverentity may include a bus 310, a processor 320, a main memory 330, a readonly memory (ROM) 340, a storage device 350, an input device 360, anoutput device 370, and a communication interface 380. Bus 310 mayinclude a path that permits communication among the elements of theclient/server entity.

Processor 320 may include a conventional processor, microprocessor, orprocessing logic that interprets and executes instructions. Main memory330 may include a random access memory (RAM) or another type of dynamicstorage device that stores information and instructions for execution byprocessor 320. ROM 340 may include a conventional ROM device or anothertype of static storage device that stores static information andinstructions for use by processor 320. Storage device 350 may include amagnetic and/or optical recording medium and its corresponding drive.

Input device 360 may include a conventional mechanism that permits anoperator to input information to the client/server entity, such as akeyboard, a mouse, a pen, voice recognition and/or biometric mechanisms,etc. Output device 370 may include a conventional mechanism that outputsinformation to the operator, including a display, a printer, a speaker,etc. Communication interface 380 may include any transceiver-likemechanism that enables the client/server entity to communicate withother devices and/or systems. For example, communication interface 380may include mechanisms for communicating with another device or systemvia a network, such as network 250.

As will be described in detail below, the client/server entity,consistent with the principles of the invention, performs certainsearching-related operations. The client/server entity may perform theseoperations in response to processor 320 executing software instructionscontained in a computer-readable medium, such as memory 330. Acomputer-readable medium may be defined as a physical or logical memorydevice and/or carrier wave.

The software instructions may be read into memory 330 from anothercomputer-readable medium, such as data storage device 350, or fromanother device via communication interface 380. The softwareinstructions contained in memory 330 may cause processor 320 to performprocesses that will be described later. Alternatively, hardwiredcircuitry may be used in place of or in combination with softwareinstructions to implement processes consistent with the principles ofthe invention. Thus, implementations consistent with the principles ofthe invention are not limited to any specific combination of hardwarecircuitry and software.

Exemplary Modeling System

FIG. 4 is an exemplary functional block diagram of a modeling system 400according to an implementation consistent with the principles of theinvention. According to one implementation, one or more of the functionsdescribed below may be performed by server 220. According to anotherimplementation, one or more of these functions may be performed by anentity separate from server 220, such as a computer associated withserver 220 or one of servers 230 or 240.

Modeling system 400 may include model generating unit 410 and modelapplying unit 420 connected to a repository 430. Repository 430 mayinclude a physical or logical memory device that stores informationassociated with documents that were crawled and indexed by, for example,server 220 (FIG. 2) or an entity separate from server 220. For example,repository 430 may store documents that form a linked database.

The document information may also, or alternatively, include featuredata associated with features of documents (“source documents”), linksin the source documents, and possibly documents pointed to by theselinks (“target documents”). Examples of features associated with a linkmight include the font size of the anchor text associated with the link;the position of the link (measured, for example, in a HTML list, inrunning text, above or below the first screenful viewed on an 800×600browser display, side (top, bottom, left, right) of document, in afooter, in a sidebar, etc.); if the link is in a list, the position ofthe link in the list; font color and/or attributes of the link (e.g.,italics, gray, same color as background, etc.); number of words inanchor text associated with the link; actual words in the anchor textassociated with the link; commerciality of the anchor text associatedwith the link; type of the link (e.g., image link); if the link isassociated with an image (i.e., image link), the aspect ratio of theimage; the context of a few words before and/or after the link; atopical cluster with which the anchor text of the link is associated;whether the link leads somewhere on the same host or domain; if the linkleads to somewhere on the same domain, whether the link URL is shorterthan the referring URL; and/or whether the link URL embeds another URL(e.g., for server-side redirection). This list is not exhaustive and mayinclude more, less, or different features associated with a link.

Examples of features associated with a source document might include theURL of the source document (or a portion of the URL of the sourcedocument); a web site associated with the source document; a number oflinks in the source document; the presence of other words in the sourcedocument; the presence of other words in a heading of the sourcedocument; a topical cluster with which the source document isassociated; and/or a degree to which a topical cluster associated withthe source document matches a topical cluster associated with anchortext of a link. This list is not exhaustive and may include more, less,or different features associated with a source document.

Examples of features associated with a target document might include theURL of the target document (or a portion of the URL of the targetdocument); a web site associated with the target document; whether theURL of the target document is on the same host as the URL of the sourcedocument; whether the URL of the target document is associated with thesame domain as the URL of the source document; words in the URL of thetarget document; and/or the length of the URL of the target document.This list is not exhaustive and may include more, less, or differentfeatures associated with a target document.

Repository 430 may also store user behavior data associated withdocuments. The user behavior data may include, for example, informationconcerning users who accessed the documents, such as navigationalactions (e.g., what links the users selected, addresses entered by theusers, forms completed by the users, etc.), the language of the users,interests of the users, query terms entered by the users, etc. In analternate implementation, the user behavior data may be stored externalto repository 430 and provided as an input to model generating unit 410.

The user behavior data might be obtained from a web browser or a browserassistant associated with clients 210. A browser assistant may includeexecutable code, such as a plug-in, an applet, a dynamic link library(DLL), or a similar type of executable object or process that operatesin conjunction with (or separately from) a web browser. The web browseror browser assistant might send information to server 220 concerning auser of a client 210.

For example, the web browser or browser assistant may record dataconcerning the documents accessed by the user and the links within thedocuments (if any) the user selected. Additionally, or alternatively,the web browser or browser assistant may record data concerning thelanguage of the user, which may be determined in a number of ways thatare known in the art, such as by analyzing documents accessed by theuser. Additionally, or alternatively, the web browser or browserassistant may record data concerning interests of the user, which may bedetermined, for example, from the favorites or bookmark list of theuser, topics associated with documents accessed by the user, or in otherways that are known in the art. Additionally, or alternatively, the webbrowser or browser assistant may record data concerning query termsentered by the user. The web browser or browser assistant may send thisdata for storage in repository 430.

In one implementation, repository 430 may also store data specific tocertain classes of users. For example, repository 430 might store datacorresponding to the language of a user class compared to the languageof the source document and the language of the link and/or targetdocument. Repository 430 might also/alternatively store datacorresponding to a topical cluster associated with the interests of theuser class compared to a topical cluster associated with the sourceand/or target documents. Repository 430 might also/alternatively storedata corresponding to a set of query words associated with the userclass compared to the content of the source and/or target documents.

Model generating unit 410 may determine link data for the variousdocument links based on information stored in repository 430. The linkdata associated with a particular link might include how often the linkwas selected and the feature data associated with the link (includingfeatures associated with the link, the source document containing thelink, and the target document referenced by the link). In oneimplementation, model generating unit 410 may analyze the user behaviordata in repository 430 to generate positive and negative instances forthe links. For example, model generating unit 410 may consider selectionof a particular link in a document as a positive instance for that linkand non-selection of the other links in the document as negativeinstances for those links. In the case where no links in a document areselected, model generating unit 410 may consider non-selection of thelinks as negative instances for the links.

To illustrate this, assume that a document W includes forward links todocuments X, Y, and Z and the user behavior data indicates that thefollowing selections occurred (W, X), (W, X), and (W, Z), where (W, X)means that the link from document W to document X was selected. In thiscase, three positive instances occurred: two for the link from W to Xand one for the link from W to Z; and six negative instances occurred:one for the link from W to X, three for the link from W to Y, and twofor the link from W to Z.

Model generating unit 410 may generate a feature vector for each of thelinks based on the link data. The feature vector associated with a linkmay be a function of the feature data associated with the link(including features associated with the link, the source documentcontaining the link, and the target document referenced by the link).For example, the feature vector might indicate the font size of theanchor text associated with the link, the web site associated with thesource document, the URL of the target document, and/or other featuredata, as described above. The feature vector may take different forms.

Model generating unit 410 may then build a model of whether a link islikely to be selected based on the link's positive and negativeinstances, the link's associated feature vector, and possibly otherinformation in repository 430. The model may be considered a dynamicmodel in that it is built from data that changes over time. Modelgenerating unit 410 may use a conventional technique, such as a naivebayes, a decision tree, logistic regression, or a hand-tailoredapproach, to form the model.

The model may include general rules and document-specific rules. Modelgenerating unit 410 may learn the general rules based on the userbehavior data and the feature vector associated with the various links.For example, model generating unit 410 may determine how users behavedwhen presented with links with different associated feature data. Fromthis information, model generating unit 410 may generate general rulesof link selection.

For example, model generating unit 410 may generate a rule thatindicates that links with anchor text greater than a particular fontsize have a higher probability of being selected than links with anchortext less than the particular font size. Additionally, or alternatively,model generating unit 410 may generate a rule that indicates that linkspositioned closer to the top of a document have a higher probability ofbeing selected than links positioned toward the bottom of the document.Additionally, or alternatively, model generating unit 410 may generate arule that indicates that when a topical cluster associated with thesource document is related to a topical cluster associated with thetarget document, the link has a higher probability of being selectedthan when the topical cluster associated with the source document isunrelated to the topical cluster associated with the target document.These rules are provided merely as examples. Model generating unit 410may generate other rules based on other types of feature data orcombinations of feature data.

Model generating unit 410 may learn the document-specific rules based onthe user behavior data and the feature vector associated with thevarious links. For example, model generating unit 410 may determine howusers behaved when presented with links of a particular source document.From this information, model generating unit 410 may generatedocument-specific rules of link selection.

For example, model generating unit 410 may generate a rule thatindicates that a link positioned under the “More Top Stories” heading onthe cnn.com web site has a high probability of being selected.Additionally, or alternatively, model generating unit 410 may generate arule that indicates that a link associated with a target URL thatcontains the word “domainpark” has a low probability of being selected.Additionally, or alternatively, model generating unit 410 may generate arule that indicates that a link associated with a source document thatcontains a popup has a low probability of being selected. Additionally,or alternatively, model generating unit 410 may generate a rule thatindicates that a link associated with a target domain that ends in “.tv”has a low probability of being selected. Additionally, or alternatively,model generating unit 410 may generate a rule that indicates that a linkassociated with a target URL that includes multiple hyphens has a lowprobability of being selected. These rules are provided merely asexamples. Model generating unit 410 may generate other document-specificrules.

Model applying unit 420 may assign weights to links based on the dynamicmodel generated by model generating unit 410. The weight of a link maybe a function of the rules applicable to the feature data associatedwith the link. A link's weight may reflect the probability that the linkwill be selected.

Model applying unit 420 may then assign ranks to documents based on theranks of their linking documents (i.e., those documents with forwardlinks to the documents), such as described in U.S. Pat. No. 6,285,999,entitled “METHOD FOR NODE RANKING IN A LINKED DATABASE,” the contents ofwhich are incorporated herein by reference. In implementationsconsistent with the principles of the invention, however, the documentranks are modified based on the dynamic weighting model describedherein. For example, ranks for the documents may be generated accordingto the function:

$\begin{matrix}{{{r(A)} = {\frac{\alpha}{N} + {\left( {1 - \alpha} \right)\left( {\frac{w_{1}{r\left( B_{1} \right)}}{B_{1}} + {\ldots\mspace{14mu}\frac{w_{n}{r\left( B_{n} \right)}}{B_{n}}}} \right)}}},} & {{Eqn}.\mspace{14mu} 1}\end{matrix}$

where A is a document for which a rank is being generated, B₁, . . . ,B_(n) are documents connected by backward links to document A, r(B₁), .. . , r(B_(n)) are the ranks of the B documents, w₁, . . . , w_(n) arethe weights assigned to the backward links, |B₁|, . . . , |B_(n)| arethe number of forward links associated with the B documents, α is aconstant in the interval [0, 1], and N is the total number of documentsin the linked database. The rank of a document may be interpreted as theprobability that a reasonable surfer will access the document afterfollowing a large number of forward links.

Model applying unit 420 may store the document ranks so that when thedocuments are later identified as relevant to a search query by a searchengine, such as search engine 225, the ranks of the documents may bequickly determined. Since links periodically appear and disappear anduser behavior data is constantly changing, model applying unit 420 mayperiodically update the weights assigned to the links and, thus, theranks of the documents.

Exemplary Processing

FIG. 5 is a flowchart of exemplary processing for determining documentranks according to an implementation consistent with the principles ofthe invention. Processing may begin with the storing of information,such as user behavior data and feature data, in a repository. Asdescribed above, documents maintained by servers connected to a networkor a combination of networks, such as the Internet, may be crawled andindexed. User behavior data and feature data may also be determined forall or a subset of the documents. The user behavior data may include,for example, information concerning users who accessed the documents,such as navigational actions of the users, the language of the users,interests of the users, query terms entered by the users, etc. Thefeature data may include features associated with source documents,links in the source documents, and possibly target documents pointed toby the links. Examples of feature data have been provided above.

Positive and negative instances for the links may be determined based oninformation in the repository (act 510). For example, the user behaviordata may be analyzed to determine which links were selected and whichlinks were not selected. Selection of a link in a document may beidentified as a positive instance for that link and non-selection of theother links in the document may be identified as negative instances forthose links. Also, selection of no links in a document may be identifiedas negative instances for all of the links in the document.

Feature vectors may be generated for the links (act 520). The featurevector associated with a link may be a function of the feature data forthe link. For example, the feature vector might include fields thatprovide feature data, such as the feature data described above,associated with the link.

A model may then be generated (act 530). The model may be generatedbased on the links' positive and negative instances, the links'associated feature vectors, and possibly other information in therepository. As described above, the model may include general rules anddocument-specific rules. The general rules are applicable acrossdocuments and the document-specific rules are applicable to specificdocuments.

Weights may be generated for links based on the model (act 540). Theweight of a link may be a function of the rules applicable to thefeature data associated with the link. A link's weight may reflect theprobability that the link will be selected.

Document ranks may then be determined based on the link weights (act550). One possible function for determining the rank of a document hasbeen described above in Eqn. 1. The rank of a document may beinterpreted as the probability that a reasonable surfer will access thedocument after following a number of forward links.

FIG. 6 is a flowchart of exemplary processing for presenting searchresults according to an implementation consistent with the principles ofthe invention. Processing may begin with a user providing search termsas a search query for searching a document corpus. In oneimplementation, the document corpus includes documents available fromthe Internet and the vehicle for searching this corpus is a searchengine, such as search engine 225 (FIG. 2). The user may provide thesearch query via web browser software on a client, such as client 210(FIG. 2).

The search query may be received by the search engine and used toidentify documents (e.g., web pages) related to the search query (acts610 and 620). A number of techniques exist for identifying documentsrelated to a search query. One such technique might include identifyingdocuments that contain the search terms as a phrase. Another techniquemight include identifying documents that contain the search terms, butnot necessarily together. Other techniques might include identifyingdocuments that contain less than all of the search terms, or synonyms ofthe search terms. Yet other techniques are known to those skilled in theart.

Ranks may be determined for the identified documents based on the model(act 630). In one implementation, document ranks are pre-calculated anddetermining the ranks of the documents may include simply looking up thedocument ranks. In another implementation, the document ranks are notpre-calculated. In this case, the ranks of the documents can bedetermined based on the model rules, as described above with regard tothe processing of FIG. 5.

The documents may then be sorted based on their ranks (act 640). Inpractice, however, a document's rank may be one of several factors usedto determine an overall rank for the document. The documents may then besorted based on their overall ranks.

Search results may be formed based on the sorted documents (act 650). Inan implementation consistent with the principles of the invention, thesearch results may include links to the documents, possibly including atextual description of the links. In another implementation, the searchresults may include the documents themselves. In yet otherimplementations, the search results may take other forms.

The search results may be provided as a HTML document, similar to searchresults provided by conventional search engines. Alternatively, thesearch results may be provided according to a protocol agreed upon bythe search engine and the client (e.g., Extensible Markup Language(XML)).

Example

FIG. 7 is a diagram of an exemplary linked database that includes threedocuments: document A, document B, and document C. As shown, document Aincludes two forward links to documents B and C and a single backwardlink from document C. Document B includes a single forward link todocument C and a single backward link from document A. Document Cincludes a single forward link to document A and two backward links fromdocuments A and B.

Assume that the backward link from document C to document A has anassociated weight of 0.5, the backward link from document A to documentB has an associated weight of 0.6, the backward link from document B todocument C has an associated weight of 0.9, and the backward link fromdocument A to document C has an associated weight of 0.4. The weightsfor these backward links may be determined based on the user behaviordata and feature data associated with the links, as described above.While a typical value for α is 0.1, assume that α is 0.5 for thisexample.

Based on Eqn. 1, the ranks of documents A-C may be determined asfollows:

$\begin{matrix}{{{r(A)} = {\frac{1}{6} + {\frac{1}{4}r(C)}}},} \\{{{r(B)} = {\frac{1}{6} + {\frac{1}{20}{r(A)}}}},{and}} \\{{r(C)} = {\frac{1}{6} + {\frac{1}{10}{r(A)}} + {\frac{9}{20}{{r(B)}.}}}}\end{matrix}$

The solution in this case is r(A)≈0.237, r(B)≈0.202, and r(C)≈0.281.

Conclusion

Systems and methods consistent with the principles of the invention maydetermine ranks for documents based on the ranks of linking documents(i.e., those documents with forward links to the documents) and adynamic link weighting model. The model may be used to adjust thecontribution that various links make in the ranking process.

The foregoing description of preferred embodiments of the presentinvention provides illustration and description, but is not intended tobe exhaustive or to limit the invention to the precise form disclosed.Modifications and variations are possible in light of the aboveteachings or may be acquired from practice of the invention.

For example, while series of acts have been described with regard toFIGS. 5 and 6, the order of the acts may be modified in otherimplementations consistent with the principles of the invention.Further, non-dependent acts may be performed in parallel.

In one implementation, server 120 may perform most, if not all, of theacts described with regard to the processing of FIGS. 5 and 6. Inanother implementation consistent with the principles of the invention,one or more, or all, of the acts may be performed by another entity,such as another server 130 and/or 140 or client 110.

It has been described that ranks are determined for documents based onuser behavior data. According to one implementation, the user behaviordata is associated with a set of users. According to anotherimplementation, the user behavior data is associated with a subset, orclass, of users. In this case, the weights assigned to the links may betailored to the user class. According to yet another implementation, theuser behavior data is associated with a single user. In this case, theweights assigned to the links may be tailored to the user.

It will also be apparent to one of ordinary skill in the art thataspects of the invention, as described above, may be implemented in manydifferent forms of software, firmware, and hardware in theimplementations illustrated in the figures. The actual software code orspecialized control hardware used to implement aspects consistent withthe principles of the invention is not limiting of the presentinvention. Thus, the operation and behavior of the aspects weredescribed without reference to the specific software code—it beingunderstood that one of ordinary skill in the art would be able to designsoftware and control hardware to implement the aspects based on thedescription herein.

No element, act, or instruction used in the present application shouldbe construed as critical or essential to the invention unless explicitlydescribed as such. Also, as used herein, the article “a” is intended toinclude one or more items. Where only one item is intended, the term“one” or similar language is used. Further, the phrase “based on” isintended to mean “based, at least in part, on” unless explicitly statedotherwise.

What is claimed is:
 1. A method performed by one or more server devices,comprising: storing, in a memory associated with the one or more serverdevices, feature data associated with features of links within documentswhere the feature data specifies one or more values for at least some ofthe features, and where the features are independent of user behavior;generating, by the one or more server devices, feature vectors for thelinks based on the stored feature data; storing, in a memory associatedwith the one or more server devices, user data relating to the links,where the user data includes user class data and user behavior data;generating, using one or more processors of the one or more serverdevices, a model that identifies a probability that a particular link,associated with one or more particular features, will be selected by aparticular user, associated with one or more user classes or behaviors,where generating the model includes: analyzing the generated featurevectors and the stored user data in relation to selections of links madeby users while viewing a document that includes one of the links, and inrelation to the one or more user classes or behaviors; and analyzing thegenerated feature vectors and the stored user data in relation to usersviewing a document that includes one of the links and not selecting theone of the links, and in relation to the one or more user classes orbehaviors; storing, in a memory associated with the one or more serverdevices, the model; and calculating, by at least one of the one or moreserver devices and based on the generated model, a probability that theparticular link will be selected by the particular user.
 2. The methodof claim 1, where generating the model further comprises: generatingrules that apply to all of the links.
 3. The method of claim 2, wheregenerating rules that apply to all of the links comprises: generating arule that indicates that first ones of the links associated with a firstattribute are associated with a higher probability of being selectedthan second ones of the links associated with a second attribute.
 4. Themethod of claim 1, where generating the model further comprises:generating rules that apply to a first document that includes at leastone of the links, and that do not apply to a second document thatincludes at least one of the links.
 5. The method of claim 4, wheregenerating the rules that apply to the first document comprises:generating a rule that indicates that a first link associated with afirst attribute within the first document is associated with a higherprobability of being selected than a second link associated with asecond attribute within the first document.
 6. The method of claim 1,further comprising: assigning a weight to the particular link, where theweight reflects the probability that the link will be selected.
 7. Themethod of claim 6, further comprising: ranking a document that includesthe particular link based on the assigned weight.
 8. The method of claim6, further comprising: updating the weight based on new user behaviordata.
 9. A system comprising: a memory to store instructions executableby one or more processors; and a processor to execute the instructionsto: store feature data associated with features of links withindocuments where the feature data specifies one or more values for atleast some of the features, and where the features are independent ofuser behavior, generate feature vectors for the links based on thestored feature data, store user data relating to: user navigationalactivity with regard to the documents accessed by one or more users, anduser class data associated with the one or more users; generate, basedon the feature data and the user data, a model that identifies aprobability that a particular link, associated with one or moreparticular features, will be selected by a particular user, associatedwith a particular navigational activity or a particular user class,where, when generating the model, the processor is to further executethe instructions to: analyze the generated feature vectors and thestored user data in relation to selections of links made by users whileviewing a document that includes one of the links and in relation to theuser class data or the user navigational activity, and analyze thegenerated feature vectors and the stored user data in relation to usersviewing a document that includes one of the links without selecting theone of the links and in relation to the user class data or the usernavigational activity; and store the model in the memory.
 10. The systemof claim 9, where the processor is to further execute the instructionsto: calculate a rank score for a document that includes the particularlink based on the identified probability.
 11. The system of claim 10,where the processor is to further execute the instructions to: order thedocument, with regard to at least one other document, based on thecalculated rank score.
 12. The system of claim 9, where the feature dataincludes at least one of: a font size of anchor text associated with alink, a position of the link within a document, a position of the linkin a list of links, a font color associated with the link, particularword in the anchor text associated with the link, a determination ofcommerciality of the anchor text a type of the link, a particular wordbefore or after the link, a topical cluster with which the anchor textis associated, whether the link leads to a target document on a samehost or domain as the document that includes the link, or whether anaddress associated with the link includes an embedded address.
 13. Anon-transitory computer-readable medium that store instructionsexecutable by at least one processor, the computer-readable mediumcomprising: one or more instructions to store, in a memory, feature dataassociated with a features of links within documents, where the featuredata specifies one or more values for at least some of the features, andwhere the features are independent of user behavior; one or moreinstructions to generate feature vectors for the links based on thestored feature data one or more instructions to store, in a memory, userdata relating to the links, where the user data includes user class dataand user behavior data one or more instructions to store, in a memory,instance data that includes: positive instances relating to when linkswere selected by a user while viewing a document that includes one ofthe links, and negative instances relating to when links within thesource documents that were not selected by the one or more users priorto ceasing to view a document that includes one of the links; one ormore instructions to generate a model that identifies a probability thata particular link, associated with one or more particular features, willbe selected by a user, associated with one or more particular userclasses or user behaviors, where the instructions to generate the rulesfurther comprise: one or more instructions to analyze the positiveinstances in relation to the generated feature vectors and the storeduser data, and one or more instructions to analyze the negativeinstances in relation to the generated feature vectors and the storeduser data; one or more instructions to store, in the memory, the model;and one or more instructions to order, based on the model, a particulardocument that includes the particular link, with respect to at least oneother document in a plurality of documents.
 14. The non-transitorycomputer-readable medium of claim 13, where the rules for the modelcomprise: a general rule applicable to a group of documents, and aspecific rule applicable only to a particular document.
 15. Thenon-transitory computer-readable medium of claim 13, further comprising:instructions to update, periodically, the rules for the model based onchanges in the user data.